Structure Optimization and Performance Analysis of a Multiple Radial Magnetorheological Valve

Due to the controllable and reversible properties of the smart magnetorheological （MR） fluid,a novel multiple radial MR valve was developed. The fluid flowchannels of the proposed MR valve were mainly composed of two annular fluid flowchannels,four radial fluid flow channels and three centric pipe fluid flowchannels. The working principle of the multiple radial MR valve was introduced in detail,and the structure optimization design was carried out using ANSYS software to obtain the optimal structure parameters. Moreover,the optimized MR valve was compared with preoptimized MR valve in terms of their magnetic flux density of radial fluid resistance gap and performance of pressure drop. The experimental test rig was set up to investigate the performance of pressure drop of the proposed MR valve under different currents applied and different loading cases. The results showthat the pressure drop between the inlet and outlet port could reach 5. 77 MPa at the applied current of 0. 8 A. Furthermore,the experimental results also indicate that the loading cases had no effect on the performance of pressure drop.

OPTIMALITY CONDITIONS AND DUALITY RESULTS FOR NONSMOOTH VECTOR OPTIMIZATION PROBLEMS WITH THE MULTIPLE INTERVAL-VALUED OBJECTIVE FUNCTION

In this paper, both Fritz John and Karush-Kuhn-Tucker necessary optimality conditions are established for a （weakly） LU-efficient solution in the considered nonsmooth multiobjective programming problem with the multiple interval-objective function. Further, the sufficient optimality conditions for a （weakly） LU-efficient solution and several duality results in Mond-Weir sense are proved under assumptions that the functions constituting the considered nondifferentiable multiobjective programming problem with the multiple interval- objective function are convex.

Multiple optimal solutions to a sort of nonlinear optimization problem

The optimization problem is considered in which the objective function is pseudolinear（both pseudoconvex and pseudoconcave） and the constraints are linear. The general expression for the optimal solutions to the problem is derived with the representation theorem of polyhedral sets, and the uniqueness condition of the optimal solution and the computational procedures to determine all optimal solutions （if the uniqueness condition is not satisfied ） are provided. Finally, an illustrative example is also given.

Distillation Sequence Optimization Considering Extractive Distillation under Multiple Conditions: A Methanol to Propylene Case Study

This study provides insights into the distillation sequence optimization of refinery system in a methanol to propylene plant with extractive distillation under multiple conditions. The simulated annealing algorithm(SA) with relative cost function was used to solve a meaningful optimization problem. It was observed that different conditions had differed on the flowsheet. Case study shows the effectiveness of the proposed method.

Bacterial graphical user interface oriented by particle swarm optimization strategy for optimization of multiple type DFACTS for power quality enhancement in distribution system

This study proposes a graphical user interface(GUI) based on an enhanced bacterial foraging optimization(EBFO) to find the optimal locations and sizing parameters of multi-type DFACTS in large-scale distribution systems.The proposed GUI based toolbox,allows the user to choose between single and multiple DFACTS allocations,followed by the type and number of them to be allocated.The EBFO is then applied to obtain optimal locations and ratings of the single and multiple DFACTS.This is found to be faster and provides more accurate results compared to the usual PSO and BFO.Results obtained with MATLAB/Simulink simulations are compared with PSO,BFO and enhanced BFO.It reveals that enhanced BFO shows quick convergence to reach the desired solution there by yielding superior solution quality.Simulation results concluded that the EBFO based multiple DFACTS allocation using DSSSC,APC and DSTATCOM is preferable to reduce power losses,improve load balancing and enhance voltage deviation index to 70%,38% and 132% respectively and also it can improve loading factor without additional power loss.

Lifetime prediction for tantalum capacitors with multiple degradation measures and particle swarm optimization based grey model

A lifetime prediction method for high-reliability tantalum （Ta） capacitors was proposed, based on multiple degradation measures and grey model （GM）. For analyzing performance degradation data, a two-parameter model based on GM was developed. In order to improve the prediction accuracy of the two-parameter model, parameter selection based on particle swarm optimization （PSO） was used. Then, the new PSO-GM（1, 2, co） optimization model was constructed, which was validated experimentally by conducting an accelerated testing on the Ta capacitors. The experiments were conducted at three different stress levels of 85, 120, and 145℃. The results of two experiments were used in estimating the parameters. And the reliability of the Ta capacitors was estimated at the same stress conditions of the third experiment. The results indicate that the proposed method is valid and accurate.

Multiple-response optimization for melting process of aluminum melting furnace based on response surface methodology with desirability function

To reduce the fuel consumption and emissions and also enhance the molten aluminum quality, a mathematical model with user-developed melting model and burning capacity model, were established according to the features of melting process of regenerative aluminum melting furnaces. Based on validating results by heat balance test for an aluminum melting furnace, CFD （computational fluid dynamics） technique, in association with statistical experimental design were used to optimize the melting process of the aluminum melting furnace. Four important factors influencing the melting time, such as horizontal angle between burners, height-to-radius ratio, natural gas mass flow and air preheated temperature, were identified by PLACKETT-BURMAN design. A steepest descent method was undertaken to determine the optimal regions of these factors. Response surface methodology with BOX-BEHNKEN design was adopted to further investigate the mutual interactions between these variables on RSD （relative standard deviation） of aluminum temperature, RSD of furnace temperature and melting time. Multiple-response optimization by desirability function approach was used to determine the optimum melting process parameters. The results indicate that the interaction between the height-to-radius ratio and horizontal angle between burners affects the response variables significantly. The predicted results show that the minimum RSD of aluminum temperature （12.13%）, RSD of furnace temperature （18.50%） and melting time （3.9 h） could be obtained under the optimum conditions of horizontal angle between burners as 64°, height-to-radius ratio as 0.3, natural gas mass flow as 599 m3/h, and air preheated temperature as 639 ℃. These predicted values were further verified by validation experiments. The excellent correlation between the predicted and experimental values confirms the validity and practicability of this statistical optimum strategy.

RESEARCH ON OPTIMIZING THE MERGING RESULTS OF MULTIPLE INDEPENDENT RETRIEVAL SYSTEMS BY A DISCRETE PARTICLE SWARM OPTIMIZATION

The result merging for multiple Independent Resource Retrieval Systems (IRRSs), which is a key component in developing a meta-search engine, is a difficult problem that still not effectively solved. Most of the existing result merging methods, usually suffered a great influence from the usefulness weight of different IRRS results and overlap rate among them. In this paper, we proposed a scheme that being capable of coalescing and optimizing a group of existing multi-sources-retrieval merging results effectively by Discrete Particle Swarm Optimization (DPSO). The experimental results show that the DPSO, not only can overall outperform all the other result merging algorithms it employed, but also has better adaptability in application for unnecessarily taking into account different IRRS's usefulness weight and their overlap rate with respect to a concrete query. Compared to other result merging algorithms it employed, the DPSO's recognition precision can increase nearly 24.6%, while the precision standard deviation for different queries can decrease about 68.3%.

Fail-Safe Topology Optimization of Continuum Structures with Multiple Constraints Based on ICM Method

Traditional topology optimization methods may lead to a great reduction in the redundancy of the optimized structure due to unexpected material removal at the critical components.The local failure in critical components can instantly cause the overall failure in the structure.More and more scholars have taken the fail-safe design into consideration when conducting topology optimization.A lot of good designs have been obtained in their research,though limited regarding minimizing structural compliance(maximizing stiffness)with given amount of material.In terms of practical engineering applications considering fail-safe design,it is more meaningful to seek for the lightweight structure with enough stiffness to resist various component failures and/or to meet multiple design requirements,than the stiffest structure only.Thus,this paper presents a fail-safe topology optimization model for minimizing structural weight with respect to stress and displacement constraints.The optimization problem is solved by utilizing the independent continuous mapping(ICM)method combined with the dual sequence quadratic programming(DSQP)algorithm.Special treatments are applied to the constraints,including converting local stress constraints into a global structural strain energy constraint and expressing the displacement constraint explicitly with approximations.All of the constraints are nondimensionalized to avoid numerical instability caused by great differences in constraint magnitudes.The optimized results exhibit more complex topological configurations and higher redundancy to resist local failures than the traditional optimization designs.This paper also shows how to find the worst failure region,which can be a good reference for designers in engineering.

Optimization of hydrogen networks with multiple impurities and impurity removal

To explore the effect of removing different impurities to hydrogen networks, an MINLP model is proposed with all matching possibilities and the trade-off between operation cost and capital cost is considered. Furthermore,the impurity remover, hydrogen distribution, compressor and pipe setting are included in the model. Based on this model, the impurity and source(s) that are in higher priority for impurity removal, the optimal targeted concentration, and the hydrogen network with the minimum annual cost can be identified. The efficiency of the proposed model is verified by a case study.

Optimization Design of High-speed Interior Permanent Magnet Motor with High Torque Performance Based on Multiple Surrogate Models

In order to obtain better torque performance of high-speed interior permanent magnet motor(HSIPMM) and solve the problem that electromagnetic optimization design is seriously limited by its mechanical strength, a complete optimization design method is proposed in this paper. The object of optimization design is a 15 kW、20000 r/min HSIPMM whose permanent magnets in rotor is segmented. Eight structural dimensions are selected as its optimization variables. After design of experiment(DOE), multiple surrogate models are fitted, a set of surrogate models with minimum error is selected by using error evaluation indexes to optimize, the NSGA-II algorithm is used to get the optimal solution. The optimal solution is verified by load test on a 15 kW, 20000 r/min HSIPMM prototype. This paper can be used as a reference for the optimization design of HSIPMM.

Novel sensitivity analysis method and dynamics optimization for multiple launch rocket systems

This study establishes the launch dynamics method,sensitivity analysis method,and multiobjective dynamic optimization method for the dynamic simulation analysis of the multiple launch rocket system(MLRS)based on the Riccati transfer matrix method for multibody systems(RMSTMM),direct differentiation method(DDM),and genetic algorithm(GA),respectively.Results show that simulation results of the dynamic response agree well with test results.The sensitivity analysis method is highly programming,the matrix order is low,and the calculation time is much shorter than that of the Lagrange method.With the increase of system complexity,the advantage of a high computing speed becomes more evident.Structural parameters that have the greatest influence on the dynamic response include the connection stiffness between the pitching body and the rotating body,the connection stiffness between the rotating body and the vehicle body,and the connection stiffnesses among 14^(#),16^(#),and 17^(#)wheels and the ground,which are the optimization design variables.After optimization,angular velocity variances of the pitching body in the revolving and pitching directions are reduced by 97.84%and 95.22%,respectively.

Decision Support through Intelligent Agent Based Simulation and Multiple Goal Based Evolutionary Optimization

Agent based simulation has successfully been applied to model complex organizational behavior and to improve or optimize aspects of organizational performance. Agents, with intelligence supported through the application of a genetic algorithm are proposed as a means of optimizing the performance of the system being modeled. Local decisions made by agents and other system variables are placed in the genetic encoding. This allows local agents to positively impact high level system performance. A simple, but non trivial, peg game is utilized to introduce the concept. A multiple objective bin packing problem is then solved to demonstrate the potential of the approach in meeting a number of high level goals. The methodology allows not only for a systems level optimization, but also provides data which can be analyzed to determine what constitutes effective agent behavior.

Multiple Response Optimization of Dimensional Accuracy of Nimonic Alloy Miniature Gear Machined on Wire Edm Using Entropy Topsis Andpareto Anova

The purpose of this research is to obtain the optimum cutting parameters to achieve the dimensional accuracy of Nimonic alloy miniature gear manufactured using Wire EDM.The cutting parameters investigated in this study are current,pulse on time(PON),pulse off time(POFF),wire tension(WT)and dielectric fluids.Ethylene glycol,nanopowder of alumina and oxygen are mixed to demineralized water to prepare novel dielectric fluids.Deviation in inner diameter,deviation in outer diameter,deviation in land and deviation in tooth width are considered to check the dimensional accuracy.Taguchi L_(16) is employed for experimental design and multiple response optimization is performed using Entropy TOPSIS and Pareto ANOVA.Results indicate that pulse on time is the most notable parameter for good dimensional accuracy followed by dielectric fluid,current,pulse off time and wire tension.Ethylene glycol mixed demineralized water is preferred for low dimensional deviation.The optimum WEDM parameters are pulse on time at 20μs,Ethylene glycol mixed demineralized water dielectric fluid,current at 3 A,pulse off time at 4μs,and wire tension at 18 N.

A combination algorithm of Chaos optimization and genetic algorithm and its application in maneuvering multiple targets data association

The most important problem in targets tracking is data association which may be represented as a sort of constraint combinational optimization problem. Chaos optimization and adaptive genetic algorithm were used to deal with the problem of multi-targets data association separately. Based on the analysis of the limitation of chaos optimization and genetic algorithm, a new chaos genetic optimization combination algorithm was presented. This new algorithm first applied the "rough" search of chaos optimization to initialize the population of GA, then optimized the population by real-coded adaptive GA. In this way, GA can not only jump out of the "trap" of local optimal results easily but also increase the rate of convergence. And the new method can also avoid the complexity and time-consumed limitation of conventional way. The simulation results show that the combination algorithm can obtain higher correct association percent and the effect of association is obviously superior to chaos optimization or genetic algorithm separately. This method has better convergence property as well as time property than the conventional ones.

Combined Life Cycle Assessment and Costing Analysis Optimization Model Using Multiple Criteria Decision Making in Earth-Resource Systems

Life Cycle Assessment (LCA) is a comprehensive method to evaluate all attributes or aspects of potential environmental impact throughout a product’s lifecycle. Financial impacts are often added to the systemic evaluation process to reflect both environmental and economic assessment. For the specific application of LCA informing design of new technologies, when numerous variables are undecided or under defined, the process of forming an inventory of complete dataset is very difficult. Accumulating the early data consumes time, and limits application of LCA to new technologies and projects. As such, LCA may not normally be associated with forecasting or guiding a design/production process with an incomplete data set. Here, a life cycle assessment optimization model (LCAO) is described for incomplete data sets, based on the life cycle inventory (LCI) hybrid method and a modified multiple criteria decision making (MCDM) approach. The approach requires data, but can proceed in the given an incomplete or uncertain data set. The model of the algorithm also shows promising results to reveal previously unknown key variables within the dataset, which can then facilitate the minimization of environmental impact while maximizing economic benefits in product design.

Numerical Optimization of Pressure Force from Multiple Jets Impinging on a Moving Curved Surface for Industrial Drying Machines

Jet force on the surface is typical for impinging jets towards the surface and it is very important in drying applications for force-sensitive surfaces. The designer should optimize the design parameters of industrial drying equipment to achieve minimum pressure force between multiple jets and a moving curved surface. SST k-ω turbulence model is used to simulate a real geometry for industrial drying applications. The SST k-ω turbulence model succeeded with reasonable accuracy in reproducing the experimental results. The jet to surface distance, jet to jet spacing, jet inlet velocity, jet angle, and surface velocity are chosen as the design parameters. For the optimization of the impinging round jet, the pressure force coefficient on the moving curved surface is set as the objective function to be minimized. The SHERPA search algorithm is used to search for the optimal point from the weighted sum of all objectives method. One correlation is developed and validated for the pressure force coefficient. It is found that the pressure force coefficient is highly dependent on the nozzle to surface distance and jet angle but relatively insensitive to jet inlet velocity, jet to jet spacing, and surface velocity. The minimum pressure force coefficient correlates with a high value of nozzle to surface distance (tenfold diameter in this analysis) and a low value of the jet angle (40? in this analysis). The agreement in the prediction of the pressure force coefficient between the numerical simulation and developed correlation is found to be reasonable and all the data points deviate from the correlation approximately 8% on average.

Multi-Material Topology Optimization for Spatial-Varying Porous Structures

This paper aims to propose a topology optimization method on generating porous structures comprising multiple materials.The mathematical optimization formulation is established under the constraints of individual volume fraction of constituent phase or total mass,as well as the local volume fraction of all phases.The original optimization problem with numerous constraints is converted into a box-constrained optimization problem by incorporating all constraints to the augmented Lagrangian function,avoiding the parameter dependence in the conventional aggregation process.Furthermore,the local volume percentage can be precisely satisfied.The effects including the globalmass bound,the influence radius and local volume percentage on final designs are exploited through numerical examples.The numerical results also reveal that porous structures keep a balance between the bulk design and periodic design in terms of the resulting compliance.All results,including those for irregular structures andmultiple volume fraction constraints,demonstrate that the proposedmethod can provide an efficient solution for multiple material infill structures.

Combined model based on optimized multi-variable grey model and multiple linear regression

The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.

Particle swarm optimization based RVM classifier for non-linear circuit fault diagnosis

A relevance vector machine (RVM) based fault diagnosis method was presented for non-linear circuits. In order to simplify RVM classifier, parameters selection based on particle swarm optimization (PSO) and preprocessing technique based on the kurtosis and entropy of signals were used. Firstly, sinusoidal inputs with different frequencies were applied to the circuit under test (CUT). Then, the resulting frequency responses were sampled to generate features. The frequency response was sampled to compute its kurtosis and entropy, which can show the information capacity of signal. By analyzing the output signals, the proposed method can detect and identify faulty components in circuits. The results indicate that the fault classes can be classified correctly for at least 99% of the test data in example circuit. And the proposed method can diagnose hard and soft faults.